On the Complexity of Rule Discovery from Distributed Data

This paper analyses the complexity of rule selection for supervised learning in distributed scenarios. The selection of rules is usually guided by a utility measure such as predictive accuracy or weighted relative accuracy. Other examples are support and confidence, known from association rule mining. A common strategy to tackle rule selection from distributed data is to evaluate rules locally on each dataset. While this works well for homogeneously distributed data, this work proves limitations of this strategy if distributions are allowed to deviate. To identify those subsets for which local and global distributions deviate may be regarded as an interesting learning task of its own, explicitly taking the locality of data into account. This task can be shown to be basically as complex as discovering the globally best rules from local data. Based on the theoretical results some guidelines for algorithm design are derived. --

Distributed archive and single access system for accelerometric event data : a NERIES initiative

We developed a common access facility to homogeneously formatted accelerometric event data and to the corresponding sheet of ground motion parameters. This paper is focused on the description of the technical development of the accelerometric data server and the link with the accelerometric data explorer. The server is the third node of the 3-tier architecture of the distributed archive system for accelerometric data. The server is the link between the data users and the accelero- metric data portal. The server follows three main steps: (1) Reading and analysis of the end-user request; (2) Processing and converting data; and (3) Archiving and updating the accelerometric data explorer. This paper presents the description of the data server and the data explorer for accessing data

OGC SWE-based Data Acquisition System Development for EGIM on EMSODEV EU Project

The EMSODEV[1] (European Multidisciplinary Seafloor and water column Observatory DEVelopment) is an EU project whose general objective is to set up the full implementation and operation of the EMSO distributed Research Infrastructure (RI), through the development, testing and deployment of an EMSO Generic Instrument Module (EGIM). This research infrastructure will provide accurate records on marine environmental changes from distributed local nodes around Europe. These observations are critical to respond accurately to the social and scientific challenges such as climate change, changes in marine ecosystems, and marine hazards. In this paper we present the design and development of the EGIM data acquisition system. EGIM is able to operate on any EMSO node, mooring line, sea bed station, cabled or non-cabled and surface buoy. In fact a central function of EGIM within the EMSO infrastructure is to have a number of ocean locations where the same set of core variables are measured homogeneously: using the same hardware, same sensor references, same qualification methods, same calibration methods, same data format and access, and same maintenance procedures.Peer ReviewedPostprint (published version

Adaptive Dynamics of Realistic Small-World Networks

Continuing in the steps of Jon Kleinberg's and others celebrated work on decentralized search in small-world networks, we conduct an experimental analysis of a dynamic algorithm that produces small-world networks. We find that the algorithm adapts robustly to a wide variety of situations in realistic geographic networks with synthetic test data and with real world data, even when vertices are uneven and non-homogeneously distributed. We investigate the same algorithm in the case where some vertices are more popular destinations for searches than others, for example obeying power-laws. We find that the algorithm adapts and adjusts the networks according to the distributions, leading to improved performance. The ability of the dynamic process to adapt and create small worlds in such diverse settings suggests a possible mechanism by which such networks appear in nature

Real-time growth rate for general stochastic SIR epidemics on unclustered networks

Networks have become an important tool for infectious disease epidemiology. Most previous theoretical studies of transmission network models have either considered simple Markovian dynamics at the individual level, or have focused on the invasion threshold and final outcome of the epidemic. Here, we provide a general theory for early real-time behaviour of epidemics on large configuration model networks (i.e. static and locally unclustered), in particular focusing on the computation of the Malthusian parameter that describes the early exponential epidemic growth. Analytical, numerical and Monte-Carlo methods under a wide variety of Markovian and non-Markovian assumptions about the infectivity profile are presented. Numerous examples provide explicit quantification of the impact of the network structure on the temporal dynamics of the spread of infection and provide a benchmark for validating results of large scale simulations.Comment: 45 pages, 8 figures, submitted to Mathematical Biosciences on 29/11/2014; Version 2: resubmitted on 15/04/2015; accepted on 17/04/2015. Changes: better explanations in introduction; restructured section 3.3 (3.3.3 added); section 6.3.1 added; more precise terminology; typos correcte

Chandra X-ray observation of the HII region Gum 31 in the Carina Nebula complex

(abridged) We used the Chandra observatory to perform a deep (70 ksec) X-ray observation of the Gum 31 region and detected 679 X-ray point sources. This extends and complements the X-ray survey of the central Carina nebula regions performed in the Chandra Carina Complex Project. Using deep near-infrared images from our recent VISTA survey of the Carina nebula complex, our Spitzer point-source catalog, and optical archive data, we identify counterparts for 75% of these X-ray sources. Their spatial distribution shows two major concentrations, the central cluster NGC 3324 and a partly embedded cluster in the southern rim of the HII region, but majority of X-ray sources constitute a rather homogeneously distributed population of young stars. Our color-magnitude diagram analysis suggests ages of ~1-2 Myr for the two clusters, whereas the distributed population shows a wider age range up to ~10 Myr. We also identify previously unknown companions to two of the three O-type members of NGC 3324 and detect diffuse X-ray emission in the region. Our results suggests that the observed region contains about 4000 young stars in total. The distributed population is probably part of the widely distributed population of ~ 1-10 Myr old stars, that was identified in the CCCP area. This implies that the global stellar configuration of the Carina nebula complex is a very extended stellar association, in which the (optically prominent) clusters contain only a minority of the stellar population.Comment: Accepted for publication in Astronomy & Astrophysics. A high quality preprint is available at http://www.usm.uni-muenchen.de/people/preibisch/publications.htm